378 lines
12 KiB
Python
378 lines
12 KiB
Python
from itertools import product
|
|
from string import ascii_lowercase
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas import (
|
|
DataFrame,
|
|
Index,
|
|
MultiIndex,
|
|
Period,
|
|
Series,
|
|
Timedelta,
|
|
Timestamp,
|
|
date_range,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestCounting:
|
|
def test_cumcount(self):
|
|
df = DataFrame([["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"])
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series([0, 1, 2, 0, 3])
|
|
|
|
tm.assert_series_equal(expected, g.cumcount())
|
|
tm.assert_series_equal(expected, sg.cumcount())
|
|
|
|
def test_cumcount_empty(self):
|
|
ge = DataFrame().groupby(level=0)
|
|
se = Series(dtype=object).groupby(level=0)
|
|
|
|
# edge case, as this is usually considered float
|
|
e = Series(dtype="int64")
|
|
|
|
tm.assert_series_equal(e, ge.cumcount())
|
|
tm.assert_series_equal(e, se.cumcount())
|
|
|
|
def test_cumcount_dupe_index(self):
|
|
df = DataFrame(
|
|
[["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=[0] * 5
|
|
)
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series([0, 1, 2, 0, 3], index=[0] * 5)
|
|
|
|
tm.assert_series_equal(expected, g.cumcount())
|
|
tm.assert_series_equal(expected, sg.cumcount())
|
|
|
|
def test_cumcount_mi(self):
|
|
mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]])
|
|
df = DataFrame([["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=mi)
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series([0, 1, 2, 0, 3], index=mi)
|
|
|
|
tm.assert_series_equal(expected, g.cumcount())
|
|
tm.assert_series_equal(expected, sg.cumcount())
|
|
|
|
def test_cumcount_groupby_not_col(self):
|
|
df = DataFrame(
|
|
[["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=[0] * 5
|
|
)
|
|
g = df.groupby([0, 0, 0, 1, 0])
|
|
sg = g.A
|
|
|
|
expected = Series([0, 1, 2, 0, 3], index=[0] * 5)
|
|
|
|
tm.assert_series_equal(expected, g.cumcount())
|
|
tm.assert_series_equal(expected, sg.cumcount())
|
|
|
|
def test_ngroup(self):
|
|
df = DataFrame({"A": list("aaaba")})
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series([0, 0, 0, 1, 0])
|
|
|
|
tm.assert_series_equal(expected, g.ngroup())
|
|
tm.assert_series_equal(expected, sg.ngroup())
|
|
|
|
def test_ngroup_distinct(self):
|
|
df = DataFrame({"A": list("abcde")})
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series(range(5), dtype="int64")
|
|
|
|
tm.assert_series_equal(expected, g.ngroup())
|
|
tm.assert_series_equal(expected, sg.ngroup())
|
|
|
|
def test_ngroup_one_group(self):
|
|
df = DataFrame({"A": [0] * 5})
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series([0] * 5)
|
|
|
|
tm.assert_series_equal(expected, g.ngroup())
|
|
tm.assert_series_equal(expected, sg.ngroup())
|
|
|
|
def test_ngroup_empty(self):
|
|
ge = DataFrame().groupby(level=0)
|
|
se = Series(dtype=object).groupby(level=0)
|
|
|
|
# edge case, as this is usually considered float
|
|
e = Series(dtype="int64")
|
|
|
|
tm.assert_series_equal(e, ge.ngroup())
|
|
tm.assert_series_equal(e, se.ngroup())
|
|
|
|
def test_ngroup_series_matches_frame(self):
|
|
df = DataFrame({"A": list("aaaba")})
|
|
s = Series(list("aaaba"))
|
|
|
|
tm.assert_series_equal(df.groupby(s).ngroup(), s.groupby(s).ngroup())
|
|
|
|
def test_ngroup_dupe_index(self):
|
|
df = DataFrame({"A": list("aaaba")}, index=[0] * 5)
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
|
|
expected = Series([0, 0, 0, 1, 0], index=[0] * 5)
|
|
|
|
tm.assert_series_equal(expected, g.ngroup())
|
|
tm.assert_series_equal(expected, sg.ngroup())
|
|
|
|
def test_ngroup_mi(self):
|
|
mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]])
|
|
df = DataFrame({"A": list("aaaba")}, index=mi)
|
|
g = df.groupby("A")
|
|
sg = g.A
|
|
expected = Series([0, 0, 0, 1, 0], index=mi)
|
|
|
|
tm.assert_series_equal(expected, g.ngroup())
|
|
tm.assert_series_equal(expected, sg.ngroup())
|
|
|
|
def test_ngroup_groupby_not_col(self):
|
|
df = DataFrame({"A": list("aaaba")}, index=[0] * 5)
|
|
g = df.groupby([0, 0, 0, 1, 0])
|
|
sg = g.A
|
|
|
|
expected = Series([0, 0, 0, 1, 0], index=[0] * 5)
|
|
|
|
tm.assert_series_equal(expected, g.ngroup())
|
|
tm.assert_series_equal(expected, sg.ngroup())
|
|
|
|
def test_ngroup_descending(self):
|
|
df = DataFrame(["a", "a", "b", "a", "b"], columns=["A"])
|
|
g = df.groupby(["A"])
|
|
|
|
ascending = Series([0, 0, 1, 0, 1])
|
|
descending = Series([1, 1, 0, 1, 0])
|
|
|
|
tm.assert_series_equal(descending, (g.ngroups - 1) - ascending)
|
|
tm.assert_series_equal(ascending, g.ngroup(ascending=True))
|
|
tm.assert_series_equal(descending, g.ngroup(ascending=False))
|
|
|
|
def test_ngroup_matches_cumcount(self):
|
|
# verify one manually-worked out case works
|
|
df = DataFrame(
|
|
[["a", "x"], ["a", "y"], ["b", "x"], ["a", "x"], ["b", "y"]],
|
|
columns=["A", "X"],
|
|
)
|
|
g = df.groupby(["A", "X"])
|
|
g_ngroup = g.ngroup()
|
|
g_cumcount = g.cumcount()
|
|
expected_ngroup = Series([0, 1, 2, 0, 3])
|
|
expected_cumcount = Series([0, 0, 0, 1, 0])
|
|
|
|
tm.assert_series_equal(g_ngroup, expected_ngroup)
|
|
tm.assert_series_equal(g_cumcount, expected_cumcount)
|
|
|
|
def test_ngroup_cumcount_pair(self):
|
|
# brute force comparison for all small series
|
|
for p in product(range(3), repeat=4):
|
|
df = DataFrame({"a": p})
|
|
g = df.groupby(["a"])
|
|
|
|
order = sorted(set(p))
|
|
ngroupd = [order.index(val) for val in p]
|
|
cumcounted = [p[:i].count(val) for i, val in enumerate(p)]
|
|
|
|
tm.assert_series_equal(g.ngroup(), Series(ngroupd))
|
|
tm.assert_series_equal(g.cumcount(), Series(cumcounted))
|
|
|
|
def test_ngroup_respects_groupby_order(self):
|
|
np.random.seed(0)
|
|
df = DataFrame({"a": np.random.choice(list("abcdef"), 100)})
|
|
for sort_flag in (False, True):
|
|
g = df.groupby(["a"], sort=sort_flag)
|
|
df["group_id"] = -1
|
|
df["group_index"] = -1
|
|
|
|
for i, (_, group) in enumerate(g):
|
|
df.loc[group.index, "group_id"] = i
|
|
for j, ind in enumerate(group.index):
|
|
df.loc[ind, "group_index"] = j
|
|
|
|
tm.assert_series_equal(Series(df["group_id"].values), g.ngroup())
|
|
tm.assert_series_equal(Series(df["group_index"].values), g.cumcount())
|
|
|
|
@pytest.mark.parametrize(
|
|
"datetimelike",
|
|
[
|
|
[Timestamp(f"2016-05-{i:02d} 20:09:25+00:00") for i in range(1, 4)],
|
|
[Timestamp(f"2016-05-{i:02d} 20:09:25") for i in range(1, 4)],
|
|
[Timedelta(x, unit="h") for x in range(1, 4)],
|
|
[Period(freq="2W", year=2017, month=x) for x in range(1, 4)],
|
|
],
|
|
)
|
|
def test_count_with_datetimelike(self, datetimelike):
|
|
# test for #13393, where DataframeGroupBy.count() fails
|
|
# when counting a datetimelike column.
|
|
|
|
df = DataFrame({"x": ["a", "a", "b"], "y": datetimelike})
|
|
res = df.groupby("x").count()
|
|
expected = DataFrame({"y": [2, 1]}, index=["a", "b"])
|
|
expected.index.name = "x"
|
|
tm.assert_frame_equal(expected, res)
|
|
|
|
def test_count_with_only_nans_in_first_group(self):
|
|
# GH21956
|
|
df = DataFrame({"A": [np.nan, np.nan], "B": ["a", "b"], "C": [1, 2]})
|
|
result = df.groupby(["A", "B"]).C.count()
|
|
mi = MultiIndex(levels=[[], ["a", "b"]], codes=[[], []], names=["A", "B"])
|
|
expected = Series([], index=mi, dtype=np.int64, name="C")
|
|
tm.assert_series_equal(result, expected, check_index_type=False)
|
|
|
|
def test_count_groupby_column_with_nan_in_groupby_column(self):
|
|
# https://github.com/pandas-dev/pandas/issues/32841
|
|
df = DataFrame({"A": [1, 1, 1, 1, 1], "B": [5, 4, np.NaN, 3, 0]})
|
|
res = df.groupby(["B"]).count()
|
|
expected = DataFrame(
|
|
index=Index([0.0, 3.0, 4.0, 5.0], name="B"), data={"A": [1, 1, 1, 1]}
|
|
)
|
|
tm.assert_frame_equal(expected, res)
|
|
|
|
def test_groupby_count_dateparseerror(self):
|
|
dr = date_range(start="1/1/2012", freq="5min", periods=10)
|
|
|
|
# BAD Example, datetimes first
|
|
ser = Series(np.arange(10), index=[dr, np.arange(10)])
|
|
grouped = ser.groupby(lambda x: x[1] % 2 == 0)
|
|
result = grouped.count()
|
|
|
|
ser = Series(np.arange(10), index=[np.arange(10), dr])
|
|
grouped = ser.groupby(lambda x: x[0] % 2 == 0)
|
|
expected = grouped.count()
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_groupby_timedelta_cython_count():
|
|
df = DataFrame(
|
|
{"g": list("ab" * 2), "delt": np.arange(4).astype("timedelta64[ns]")}
|
|
)
|
|
expected = Series([2, 2], index=Index(["a", "b"], name="g"), name="delt")
|
|
result = df.groupby("g").delt.count()
|
|
tm.assert_series_equal(expected, result)
|
|
|
|
|
|
def test_count():
|
|
n = 1 << 15
|
|
dr = date_range("2015-08-30", periods=n // 10, freq="T")
|
|
|
|
df = DataFrame(
|
|
{
|
|
"1st": np.random.choice(list(ascii_lowercase), n),
|
|
"2nd": np.random.randint(0, 5, n),
|
|
"3rd": np.random.randn(n).round(3),
|
|
"4th": np.random.randint(-10, 10, n),
|
|
"5th": np.random.choice(dr, n),
|
|
"6th": np.random.randn(n).round(3),
|
|
"7th": np.random.randn(n).round(3),
|
|
"8th": np.random.choice(dr, n) - np.random.choice(dr, 1),
|
|
"9th": np.random.choice(list(ascii_lowercase), n),
|
|
}
|
|
)
|
|
|
|
for col in df.columns.drop(["1st", "2nd", "4th"]):
|
|
df.loc[np.random.choice(n, n // 10), col] = np.nan
|
|
|
|
df["9th"] = df["9th"].astype("category")
|
|
|
|
for key in ["1st", "2nd", ["1st", "2nd"]]:
|
|
left = df.groupby(key).count()
|
|
right = df.groupby(key).apply(DataFrame.count).drop(key, axis=1)
|
|
tm.assert_frame_equal(left, right)
|
|
|
|
|
|
def test_count_non_nulls():
|
|
# GH#5610
|
|
# count counts non-nulls
|
|
df = DataFrame(
|
|
[[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, np.nan]],
|
|
columns=["A", "B", "C"],
|
|
)
|
|
|
|
count_as = df.groupby("A").count()
|
|
count_not_as = df.groupby("A", as_index=False).count()
|
|
|
|
expected = DataFrame([[1, 2], [0, 0]], columns=["B", "C"], index=[1, 3])
|
|
expected.index.name = "A"
|
|
tm.assert_frame_equal(count_not_as, expected.reset_index())
|
|
tm.assert_frame_equal(count_as, expected)
|
|
|
|
count_B = df.groupby("A")["B"].count()
|
|
tm.assert_series_equal(count_B, expected["B"])
|
|
|
|
|
|
def test_count_object():
|
|
df = DataFrame({"a": ["a"] * 3 + ["b"] * 3, "c": [2] * 3 + [3] * 3})
|
|
result = df.groupby("c").a.count()
|
|
expected = Series([3, 3], index=Index([2, 3], name="c"), name="a")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
df = DataFrame({"a": ["a", np.nan, np.nan] + ["b"] * 3, "c": [2] * 3 + [3] * 3})
|
|
result = df.groupby("c").a.count()
|
|
expected = Series([1, 3], index=Index([2, 3], name="c"), name="a")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_count_cross_type():
|
|
# GH8169
|
|
vals = np.hstack(
|
|
(np.random.randint(0, 5, (100, 2)), np.random.randint(0, 2, (100, 2)))
|
|
)
|
|
|
|
df = DataFrame(vals, columns=["a", "b", "c", "d"])
|
|
df[df == 2] = np.nan
|
|
expected = df.groupby(["c", "d"]).count()
|
|
|
|
for t in ["float32", "object"]:
|
|
df["a"] = df["a"].astype(t)
|
|
df["b"] = df["b"].astype(t)
|
|
result = df.groupby(["c", "d"]).count()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_lower_int_prec_count():
|
|
df = DataFrame(
|
|
{
|
|
"a": np.array([0, 1, 2, 100], np.int8),
|
|
"b": np.array([1, 2, 3, 6], np.uint32),
|
|
"c": np.array([4, 5, 6, 8], np.int16),
|
|
"grp": list("ab" * 2),
|
|
}
|
|
)
|
|
result = df.groupby("grp").count()
|
|
expected = DataFrame(
|
|
{"a": [2, 2], "b": [2, 2], "c": [2, 2]}, index=Index(list("ab"), name="grp")
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_count_uses_size_on_exception():
|
|
class RaisingObjectException(Exception):
|
|
pass
|
|
|
|
class RaisingObject:
|
|
def __init__(self, msg="I will raise inside Cython"):
|
|
super().__init__()
|
|
self.msg = msg
|
|
|
|
def __eq__(self, other):
|
|
# gets called in Cython to check that raising calls the method
|
|
raise RaisingObjectException(self.msg)
|
|
|
|
df = DataFrame({"a": [RaisingObject() for _ in range(4)], "grp": list("ab" * 2)})
|
|
result = df.groupby("grp").count()
|
|
expected = DataFrame({"a": [2, 2]}, index=Index(list("ab"), name="grp"))
|
|
tm.assert_frame_equal(result, expected)
|